在二次条件和阻断过程中,神经网络中融合和独立路径的比较
Giselle M Castañeda1, José E Burgos1, Jonathan Buriticá1
1Universidad de Guadalajara, Centro de Estudios e Investigaciones en Comportamiento.
Journal of experimental psychology. Animal learning and cognition
|October 27, 2025
概括
神经网络路径影响学习. 融合途径更好地模拟阻断 (Blk),而独立途径更好地模拟二次条件 (SOC),影响我们对学习中强化学习历史的理解.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 之前的强化学习史显著塑造了对后续刺激的学习.
- 阻断 (Blk) 和二次条件 (SOC) 是说明这种影响的关键现象,显示条件反应的增强或抑制.
- 了解这些关联式学习过程背后的神经机制至关重要.
研究的目的:
- 在模拟Blk和SOC的神经网络模型中研究融合与独立路径的差异性作用.
- 为了比较五个不同的网络架构的有效性,在这些学习现象的建模中具有不同的融合连接性.
- 探索网络架构如何影响基于强化历史的协同学习模拟.
主要方法:
- 开发和比较五个feedforward神经网络架构,具有可控的融合连接度.
- 在这些网络模型中模拟阻断 (Blk) 和二级调节 (SOC).
- 在复制Blk和SOC的既定行为发现时,对网络性能进行定量分析.
主要成果:
- 具有更高程度的融合连接的网络在模拟阻断 (Blk) 中表现出更高的性能.
- 以独立路径为特征的网络在建模二阶条件 (SOC) 中表现出更高的准确性.
- 观察到的例外表明,路径架构和特定的学习范式之间存在微妙的相互作用.
结论:
- 神经通路的架构,特别是融合,在确定模型模拟不同协会学习现象的能力方面发挥着关键作用,如Blk和SOC.
- 研究结果表明,融合途径更符合阻断机制,而独立途径更好地捕捉了二次条件.
- 这些结果对深度学习模型的概念化有影响,并可以为未来的神经行为研究假设提供信息.
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